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Intelligent Categorization Method for Diagnosing Cardiovascular Diseases Hierarchically

Mubo Chen and Mingchui Dong
Dept. of ECE, FST, University of Macau, Macau S. A. R., China

Abstract—For detecting cardiovascular diseases (CVDs) hierarchically, it has been verified that a disease-oriented categorization method is necessary and valid. Such method should be able to categorize 32 hemodynamic parameters (HDPs), 9 symptom parameters (SPs), and 6 physiological parameters (PPs) into different groups aiming at diagnosing different CVDs efficiently. To this end, IGAECM, a new categorization method based on information gain attribute evaluation with two pivotal steps is proposed. First, compute the information gain (IG) of each HDP, SP or PP variable, and discard redundant variables with zero IG. Then according to IG of each variable, search and categorize the remaining HDP, SP and PP variables into different groups automatically. Compared with previous proposed method CARTCM (categorization method based on classification and regression tree), it has three main advantages: (i) an IG-based searching strategy is proposed to group HDPs, SPs & PPs while CARTCM uses a simple threshold strategy; (ii) automatically determines not only the number of groups but also the quantity and types of parameters relevant to CVDin each group, while CARTCM contains user manually defined thresholds; (iii) discards the redundant variables thus reduces the computing complexity while CARTCM does not. The effectiveness and adaptability of such method is demonstrated and tested by applying it in diagnosing 5 most common and important CVDs successfully.

Index Terms—cardiovascular diseases, disease-oriented, information gain based searching strategy, multi-label learning

Cite: Mubo Chen and Mingchui Dong, "Intelligent Categorization Method for Diagnosing Cardiovascular Diseases Hierarchically," Jounal of Automation and Control Engineering, Vol. 3, No. 6, pp. 512-518, December, 2015. doi: 10.12720/joace.3.6.512-518
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